Predicting coordinated group movements of sharks with limited observations using AUVs Article Swipe
Cherie Ho
,
Kimberly Joly
,
Andrew P. Nosal
,
Christopher G. Lowe
,
Christopher M. Clark
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1145/3019612.3019711
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1145/3019612.3019711
This paper presents a method for modeling and then tracking the 2D planar size, location, orientation, and number of individuals of an animal aggregation using Autonomous Underwater Vehicles (AUVs). It is assumed that the AUVs are equipped with sensors that can measure the position states of a subset of individuals from within the aggregation being tracked. A new aggregation model based on provably stable Markov Process Matrices is shown as a viable model for representing aggregations. Then, a multi-stage state estimation architecture based on Particle Filters is presented that can estimate the time-varying model parameters in real-time using sensor measurements obtained by AUVs. To validate the approach, a historical data set is used consisting of >100 shark trajectories from a leopard shark aggregation observed in the La Jolla, CA coast area. The method is generalizable to any stable group movement model constructed using a Markov Matrix. Simulation results show that, when at least 40+ of sharks are tagged, the estimated number of sharks in the aggregation has an error of 6+. This error increased to 27+ when the system was tested with real data.
Related Topics
Concepts
Computer science
Markov chain
Hidden Markov model
Position (finance)
Underwater
Markov process
Discrete time and continuous time
Set (abstract data type)
Tracking (education)
Real-time computing
Artificial intelligence
Geography
Mathematics
Statistics
Machine learning
Archaeology
Pedagogy
Economics
Programming language
Finance
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3019612.3019711
- http://dl.acm.org/ft_gateway.cfm?id=3019711&type=pdf
- OA Status
- gold
- Cited By
- 4
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2562012544
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2562012544Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3019612.3019711Digital Object Identifier
- Title
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Predicting coordinated group movements of sharks with limited observations using AUVsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-04-03Full publication date if available
- Authors
-
Cherie Ho, Kimberly Joly, Andrew P. Nosal, Christopher G. Lowe, Christopher M. ClarkList of authors in order
- Landing page
-
https://doi.org/10.1145/3019612.3019711Publisher landing page
- PDF URL
-
https://dl.acm.org/ft_gateway.cfm?id=3019711&type=pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://dl.acm.org/ft_gateway.cfm?id=3019711&type=pdfDirect OA link when available
- Concepts
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Computer science, Markov chain, Hidden Markov model, Position (finance), Underwater, Markov process, Discrete time and continuous time, Set (abstract data type), Tracking (education), Real-time computing, Artificial intelligence, Geography, Mathematics, Statistics, Machine learning, Archaeology, Pedagogy, Economics, Programming language, Finance, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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